We use a dataset from Kaggle to study obesity over time.
According to the WHO, a person is overweight when his or her body mass index (BMI) is above 25 and obese when it is above 30. The problem has reached epidemic proportions as of 2017, more than 4 million people were dying each year from overweight or obesity, according to a Global Burden of Disease study.
The dataset has 6 variables:
Here is an extract from our obesity dataset:
| Country | Year | Sex | Obesity | Std_Dvt |
|---|---|---|---|---|
| Afghanistan | 1975 | Both sexes | 0.5 | [0.2-1.1] |
| Afghanistan | 1975 | Male | 0.2 | [0.0-0.6] |
| Afghanistan | 1975 | Female | 0.8 | [0.2-2.0] |
| Afghanistan | 1976 | Both sexes | 0.5 | [0.2-1.1] |
| Afghanistan | 1976 | Male | 0.2 | [0.0-0.7] |
In order to be able to make maps we also imported the World dataset from R. This dataset allows us to have the names of the countries in ISO3 format and to obtain various information about the countries, such as the number of inhabitants. Here is an extract of the dataset:
| CountryISO | name | sovereignt | continent | area | geometry |
|---|---|---|---|---|---|
| AFG | Afghanistan | Afghanistan | Asia | 652860.00 [km^2] | MULTIPOLYGON (((61.21082 35… |
| AGO | Angola | Angola | Africa | 1246700.00 [km^2] | MULTIPOLYGON (((16.32653 -5… |
| ALB | Albania | Albania | Europe | 27400.00 [km^2] | MULTIPOLYGON (((20.59025 41… |
| ARE | United Arab Emirates | United Arab Emirates | Asia | 71252.17 [km^2] | MULTIPOLYGON (((51.57952 24… |
| ARG | Argentina | Argentina | South America | 2736690.00 [km^2] | MULTIPOLYGON (((-65.5 -55.2… |